Energy flow prediction for electric systems including photovoltaic solar systems

ABSTRACT

Methods, systems, and computer storage media are disclosed for determining electric energy flow predictions for electric systems including photovoltaic solar systems. In some examples, a method is performed by a computer system and includes supplying a consumption time series and a predicted production time series for an electric system to a machine-learning predictor trained during a prior training phase using electric energy consumption training data and photovoltaic production training data. The consumption time series has a first data resolution, and the electric energy consumption training data and the photovoltaic production training data have a second data resolution greater than the first data resolution. The method includes determining, using an output of the machine-learning predictor, a predicted import time series of electric import values each specifying an amount of electric energy predicted to be imported by the electric system with a prospective photovoltaic solar system installed.

BACKGROUND

The subject matter described in this specification relates generally tocomputer systems programmed for determining electric energy flowpredictions for electric systems including photovoltaic solar systems.

Photovoltaic cells, commonly known as solar cells, are devices forconversion of solar radiation into electric energy. A photovoltaic solarsystem typically includes a panel of multiple photovoltaic cells on aframe, one or more inverters, and interconnection wiring. A photovoltaicsolar system can also include other optional components such asbatteries, solar trackers, and a meteorological station. The frame canbe mounted on top of a building and the other components can be locatedon the outside or inside of the building to interface with an electricgrid of the building and, in some cases, a utility electric grid. Autility company may charge a customer based on both energy consumptionfrom the utility electric grid and energy production by the photovoltaicsolar system.

SUMMARY

A computer system is programmed for determining electric energy flowpredictions for electric systems including photovoltaic solar systems.The computer system can be useful, e.g., for determining accuratepredictions of electric import values based on energy consumption datarecorded at a lower resolution than a metering resolution used by anelectric utility. In some examples, the computer system includes memorystoring one or more computer programs and one or more processorsconfigured to execute the one or more computer programs to perform amethod for electric energy flow prediction.

In some examples, the method includes supplying a consumption timeseries and a predicted production time series for an electric system toa machine-learning predictor trained during a prior training phase usingelectric energy consumption training data and photovoltaic productiontraining data. The consumption time series has a first data resolution,and the electric energy consumption training data and the photovoltaicproduction training data have a second data resolution greater than thefirst data resolution. The method includes determining, using an outputof the machine-learning predictor, a predicted import time series ofelectric import values each specifying an amount of electric energypredicted to be imported by the electric system with a prospectivephotovoltaic solar system installed.

The computer systems described in this specification may be implementedin hardware, software, firmware, or combinations of hardware, softwareand/or firmware. The computer systems described in this specificationmay be implemented using a non-transitory computer storage mediumstoring one or more computer programs that, when executed by one or moreprocessors, cause the one or more processors to perform the method forelectric energy flow prediction for photovoltaic solar systems. Computerstorage media suitable for implementing the computer systems describedin this specification include non-transitory computer storage media,such as disk memory devices, chip memory devices, programmable logicdevices, random access memory (RAM), read only memory (ROM), opticalread/write memory, cache memory, magnetic read/write memory, flashmemory, and application specific integrated circuits. A computer storagemedium used to implement the computer systems described in thisspecification may be located on a single device or computing platform ormay be distributed across multiple devices or computing platforms.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example electric system;

FIGS. 2A-C are example electric energy consumption and photovoltaicproduction plots;

FIG. 3 is a block diagram illustrating an example network environment ofa photovoltaic predictor computer system;

FIG. 4 is a block diagram of an example computer system programmed fortraining a machine-learning predictor during a training phase;

FIG. 5 is a block diagram of an example computer system programmed fordetermining electric energy flow predictions for electric systemsincluding photovoltaic solar systems; and

FIG. 6 is a flow diagram of an example method for determining electricenergy flow predictions for electric systems including photovoltaicsolar systems.

DETAILED DESCRIPTION

FIG. 1 is a diagram of an example electric system 100. The electricsystem 100 includes one or more photovoltaic solar panels 102 mounted ona building 104 and one or more inverters 106 coupled to the photovoltaicsolar panels 102. The electric system 100 also includes a connection 108to a utility grid, an electric meter 110 for the connection 108 to theutility grid, and an electric panel 112 for the electric system 100.

When the sun is shining on the photovoltaic solar panels 102, thephotovoltaic solar panels 102 produce direct current (DC) electric powerand provide the DC power to the inverters 106. In response, theinverters 106 provide alternating current (AC) power for consumption byone or more loads on the electric system 100 or for exporting to theconnection 108 to the utility grid. The loads can be located, e.g.,inside or outside the building 104.

When the loads on the electric system 100 are consuming more electricenergy than the photovoltaic solar panels 102 are producing, theelectric system 100 imports electric energy from the connection 108 tothe utility grid. When the loads are consuming less electric energy thanthe photovoltaic solar panels 102 are producing, the electric system 100can export the excess electric energy to the connection 108 to theutility grid. Some electric utilities do not allow or compensate forenergy export, so that when the loads are consuming less electric energythan the photovoltaic solar panels 102 are producing, the excesselectric energy may be unused.

When a manager of the electric system 100 considers installing aprospective photovoltaic solar system with the electric system 100, themanager faces a number of choices. The manager selects an installer ormanufacturer or both for the photovoltaic solar panels 102. The managermay also select a physical configuration of the photovoltaic solarsystem (e.g., number and dimensions of solar panels, orientation ofsolar panels, photovoltaic efficiency of solar cells in the solarpanels), a financing option (e.g., cash or lease), and a utility ratestructure (e.g., fixed, tiered, or time of use).

A utility rate structure typically includes one or more rules thatspecify how electric usage will be billed to the electric utility. Forexample, in a fixed rate structure, total electric usage over a timeperiod is multiplied by a fixed billing rate to determine the cost ofthe usage over that time period. In a tiered rate structure, electricusage is billed at one rate up to a certain amount and then billed at adifferent rate over that amount, and there may be several tiers eachhaving a respective billing rate. In a time of use structure, electricusage can be billed based on the time of day of consumption, e.g., sothat a higher billing rate applies during peak consumption hours.

Some utility rate structures impose a charge based on net-meteringbalancing. For example, suppose that the electric meter 110 increasesthe metered consumption while the electric system 100 is importingenergy and decreases the metered consumption while the electric system100 is exporting energy, e.g., the electric meter 110 may spinbackwards. The electric utility may use a net-metering balancing ratestructure and record the metered consumption at periodic intervals,e.g., once per hour or day or month, and then charge the manager of theelectric system 100 based on the recorded consumption.

Furthermore, some utility rate structures impose a charge based on eachunit of energy imported in a metered interval in addition tonet-metering balancing. For example, under such a rate structure, theelectric utility may charge for consumption during a period of time evenwhen the photovoltaic solar panels 102 produced as much energy in totalas the loads consumed over the period of time, if the loads at any timeduring the period of time consumed more energy than the photovoltaicsolar panels 102 were producing at that time, resulting in a non-zeroimport from the utility grid.

For an electric system manager considering installing a prospectivephotovoltaic solar system under such a rate structure, determiningaccurate predictions of instantaneous electric import values can beuseful for various purposes. For example, accurate predictions ofinstantaneous electric import values can be useful in determiningwhether or not to install the prospective photovoltaic solar system,determining an appropriate physical configuration for the prospectivephotovoltaic solar system, and predicting cost reductions or otherfinancial information. Determining accurate predictions of instantaneouselectric import values, however, may be challenging where availableenergy consumption data has a lower resolution than a meteringresolution used by the electric utility.

FIGS. 2A-C are example electric energy consumption and photovoltaicproduction plots. FIGS. 2A-C illustrate at least some of the challengesin determining accurate predictions of instantaneous electric importvalues.

FIG. 2A shows instantaneous electric energy import and export data foran example electric system. The data is instantaneous in that the datais recorded at a high resolution, e.g., the data is spaced apart by timeintervals that are shorter than an hour. FIG. 2A shows a first chart 200of a production time series of electric production values for an examplephotovoltaic solar system installed with the electric system and asecond chart 202 of a consumption time series of electric consumptionvalues for one or more loads of the electric system. The consumptiontime series corresponds to the production time series in that thetimelines of each chart 200 and 202 refer to the same times, i.e., thecharts 200 and 202, taken together, show the electric energy beingimported or exported by the system as a whole for each time on thecharts 200 and 202.

The first chart 200 shows that the photovoltaic solar system produced 1kW constantly over the first half hour of an hour time period and zerokW for the second half hour of the hour time period. The second chart202 shows that the loads consumed zero kW constantly over first halfhour of the hour time period and consumed 2 kW constantly for the secondhalf hour of the hour time period. Therefore, over the hour time period,the electric system imported 1 kWh, which happened during the secondhalf hour. Over the hour time period, the electric system exported 0.5kWh, which happened during the first half hour.

FIG. 2B shows low resolution energy import and export data for theexample electric system over the same period of time as shown in FIG.2A. The data is low resolution because the data is spaced apart by timeintervals that are longer than a metering resolution used by an electricutility, e.g., the data is recorded at one hour intervals whereas themetering resolution is instantaneous. FIG. 2B shows a first chart 204 ofa production time series for the example photovoltaic solar system and asecond chart 202 of a consumption time series for the loads of theelectric system. The consumption time series corresponds to theproduction time series.

The first chart 204 shows that, at the low resolution, the photovoltaicsolar system appeared to produce 0.5 kWh, and the second chart showsthat, at the low resolution, the loads appeared to consume 1 kWh.Therefore, taken together, the electric system appeared, at the lowresolution, to import 0.5 kWh and export 0 kWh over the hour. Under autility rate structure that imposed charges based on net-meteringbalancing, the charge for the illustrated hour would be the sameregardless of whether the data was high resolution, as in FIG. 2A, orlow resolution, as in FIG. 2B. The high resolution data in FIG. 2A,however, shows that the charges would be higher under a utility ratestructure that imposed charges based on each unit of energy imported ina metered interval in addition to net-metering balancing, whereas thelow resolution data in FIG. 2B lacks this information.

FIG. 2C further illustrates the difference by showing a first chart 220of example electric energy flows at a high resolution (e.g., five minuteintervals) and a second chart 226 of the example electric energy flowsat a low resolution (e.g., hourly intervals). The first chart 220 showsa production time series and a consumption series. Between hours 13 and14, the amount of energy imported is shown as the area 222 between thetwo curves where consumption is greater and the amount of energyexported is shown as the area 224 between the two curves whereproduction is greater.

The second chart 226 shows the production time series and theconsumption series downsampled to the low resolution. Between hours 13and 14, the amount of energy imported is shown as the difference 228between the two curves. Determining predicted import values using thesecond chart 226 would potentially lack the information regarding theenergy imported shown as the area 222 in the first chart 220. In caseswhere available data is recorded at the low resolution, predicted importvalues may not be accurate, which can significantly alter energy andfinancial predictions in certain utility rate structures, e.g., under autility rate structure that imposes charges based on each unit of energyimported in a metered interval in addition to net-metering balancing.

The differences between the high resolution data and the low resolutiondata can result in self-consumption error. Self-consumption per unittime is the amount of produced photovoltaic electric energy that wasconsumed by the electrical system per unit time. Self-consumption can beexpressed as, e.g., a function of consumption/production ratio orminimum of consumption and production values, and as a percentage or asan absolute value. In some examples, the computer systems described inthis specification are programmed to determine self-consumption as anabsolute value of minimum of consumption and production values. Thecomputer systems can be programmed to determine self-consumption erroras the difference between a self-consumption determined using highresolution data and a self-consumption determined using low resolutiondata.

FIG. 3 is a block diagram illustrating an example network environment300 of a photovoltaic predictor computer system 302 programmed fordetermining electric energy flow predictions for electric systemsincluding photovoltaic solar systems, e.g., the example electric system100 of FIG. 1.

A manager 304 of an electric system is considering a prospectivephotovoltaic solar system. For example, the manager 304 may be in aprocurement process for the photovoltaic solar system, or the manager304 may have recently installed the photovoltaic solar system and lackscertain kinds of predictions regarding the installed system. The manager304 can be a residential homeowner or a commercial building manager orany appropriate individual associated with the electric system. Themanager 304 consults with an installer 306 of photovoltaic solarsystems.

The installer 306 operates a user device 308 to communicate with thephotovoltaic predictor computer system 302 over a data communicationsnetwork 312. The manager 304 may also operate a user device 310. Theuser devices 308 and 310 can each be any appropriate computer system,e.g., a computer system with a display and a user input device such as apersonal computer, laptop, or tablet computer. The photovoltaicpredictor computer system 302 can be implemented as a cloud-basedservice, e.g., as a server implemented on a distributed computingplatform.

The photovoltaic predictor computer system 302 is programmed to importelectric energy flow data from one or more of various sources includingthe installer's user device 308, the manager's user device 310, acomputer system 314 located at a site of the electric system and coupledto an electric meter of the electric system, a utility computer system316, and an external energy information source computer system 318. Thephotovoltaic predictor computer system 302 is programmed for determiningenergy flow predictions and presenting results for display on theinstaller's user device 308 or the manager's user device 310 or both.The results can include, for example, a target physical configuration ofthe prospective photovoltaic solar system or a predicted energy costreduction or both.

In some examples, the installer 306 executes a web browser on the userdevice 308 and enters a uniform resource locator (URL) into the webbrowser for the photovoltaic predictor computer system 302. Thephotovoltaic predictor computer system 302 executes a web server thatprovides a graphical user interface (GUI) to the user device 308, e.g.,as one or more web pages which can be comprised of hypertext markuplanguage (HTML) files and image files. The installer 306 can then supplydata to the photovoltaic predictor computer system 302 using the GUI,and the photovoltaic predictor computer system 302 can provide resultson one or more display screens of the GUI. Based on the results, themanager 304 may have the installer 306 install the target physicalconfiguration of the prospective photovoltaic solar system with theelectric system.

The photovoltaic predictor computer system 302 is programmed forsupplying a consumption time series and a predicted production timeseries for the electric system to a machine-learning predictor trainedduring a prior training phase using electric energy consumption trainingdata and photovoltaic production training data. The consumption timeseries has a first data resolution, and the electric energy consumptiontraining data and the photovoltaic production training data have asecond data resolution greater than the first data resolution. Thephotovoltaic predictor computer system 302 determines, using an outputof the machine-learning predictor, a predicted import time series ofelectric import values each specifying an amount of electric energypredicted to be imported by the electric system with the prospectivephotovoltaic solar system installed.

FIG. 4 is a block diagram of an example computer system 400 programmedfor training a machine-learning predictor 414 during a training phase.The computer system 400 includes one or more processors 402 and memory404 storing one or more computer programs for execution by theprocessors 402.

The memory 404 stores electric energy consumption training data 406 andphotovoltaic production training data 408. The electric energyconsumption training data 406 and the photovoltaic production trainingdata 408 include energy values spaced apart by time intervals at a highresolution, e.g., a resolution greater than or equal to a meteringresolution used by an electric utility. The electric energy consumptiontraining data 406 can include consumption training sets, with each setincluding a time series of electric energy consumption values.

The photovoltaic production training data 408 then includes productiontraining sets each corresponding to a respective consumption trainingset. Each production training set includes a time series of photovoltaicproduction values. The electric energy consumption training data 406 andthe photovoltaic production training data 408 can include simulated dataor recorded data from live systems or both.

The computer system 400 includes a model-building trainer 410, a downsampler 412, and a machine-learning predictor 414 which can each beimplemented as one or more computer programs stored in the memory 404.In operation, the model-building trainer 410 supplies the electricenergy consumption training data 406 and the photovoltaic productiontraining data 408 to the machine-learning predictor 414. The downsampler 412 downsamples the electric energy consumption training data406 and the photovoltaic production training data 408 to a lowresolution, e.g., a resolution used by electric energy consumptionmetering systems. The model-building trainer 410 supplies thedownsampled electric energy consumption training data and thedownsampled photovoltaic production training data to themachine-learning predictor 414.

The model-building trainer 410 configures the machine-learning predictor414 to build a model to minimize the self-consumption error between theelectric energy consumption training data 406, the photovoltaicproduction training data 408, and the downsampled electric energyconsumption training data and the downsampled photovoltaic productiontraining data. The machine-learning predictor 414 can be implementedusing any appropriate computer code for automating analytical modelbuilding. For example, the machine-learning predictor 414 can beimplemented by computer code for carrying out one or more structuredprediction algorithms.

The model-building trainer 410 can configure the machine-learningpredictor 414 to build the model by determining self-consumption erroras a difference between a first self-consumption determined at the lowresolution and a second self-consumption determined at the highresolution. For example, the model-building trainer 410 can configurethe machine-learning predictor 414 to determine the firstself-consumption as a minimum of a downsampled consumption training dataand a corresponding downsampled production training data. Themodel-building trainer 410 can then configure the machine-learningpredictor 414 to determine the second self-consumption as a minimum ofthe consumption training data at second resolution and the productiontraining data at second resolution.

In some examples, configuring the machine-learning predictor to buildthe model to minimize self-consumption error comprises groupingproduction and consumption values for each time interval of thedownsampled electric energy consumption training data and thedownsampled photovoltaic production training data into dataclassification bins. The data classification bins are established basedon a difference between the consumption value and the production valuein the time interval. Then, the data classification bins are mapped toan average self-consumption error calculated as the average ofself-consumption errors of all production and consumption values groupedinto the data classification bins.

In some examples, the electric energy consumption training data 406 andthe photovoltaic production training data 408 are divided into portionscorresponding to photovoltaic solar system conditions, e.g., geographiclocations or seasons of the year or both. Then, the model-buildingtrainer 410 can configure the machine-learning predictor 414 to buildmodels for each portion. For example, the training data can be dividedinto four portions each corresponding to a respective season of theyear, and configuring the machine-learning predictor 414 to build themodel includes configuring the machine-learning predictor 414 toseparately model each of the portions corresponding to the seasons.

FIG. 5 is a block diagram of an example computer system 500 programmedfor determining electric energy flow predictions for electric systemsincluding photovoltaic solar systems. The computer system 500 canimplement the photovoltaic predictor computer system 302 of FIG. 3.

The computer system 500 includes one or more processors 502 and memory504 storing one or more computer programs for execution by theprocessors 502. The computer system 500 includes a data importer 506, aphotovoltaic production simulator 508, and a consumption simulator 510which can each be implemented as one or more computer programs stored inthe memory 504. The computer system 500 further includes themachine-learning predictor 414 of FIG. 4, a graphical user interface(GUI) 514, and a photovoltaic system analyzer 512 which can each beimplemented as one or more computer programs stored in the memory 504.

In operation, the data importer 506 receives a consumption time seriesof electric consumption values for an electric system and a predictedproduction time series of electric production values for a prospectivephotovoltaic solar system for installation with the electric system. Theelectric consumption values of the consumption time series are spacedapart by time intervals at a low resolution. The electric productionvalues may also be spaced apart by time intervals at the low resolutionor the electric production values may be downsampled to the lowresolution.

For example, the data importer 506 can receive a measured consumptiontime series from a metering system of the electric system (e.g.,computer system 314 in FIG. 3) or from a utility metering computersystem remote from the electric system (e.g., computer system 316 inFIG. 3). In another example, the data importer 506 can execute theconsumption simulator 510 to computationally simulate the electricsystem. The consumption simulator 510 can simulate electric energyconsumption of the electric system based on, e.g., a geographic locationof the electric system and one or more physical characteristics of abuilding housing at least a portion of the electric system.

In some examples, the consumption simulator 510 uses retrieves externaldata from an external source, e.g., the external energy informationsource computer system 318 of FIG. 3. The external data can includevarious appropriate datasets such as commercial and residential hourlyload profiles for various geographic locations, electric utility ratestructures organized by geographic locations, and a library of uniquelyidentifiable building components that represent physical characteristicsof buildings such as roofs, walls, and windows.

Receiving the predicted production time series can include executing thephotovoltaic production simulator 508 to computationally simulate theprospective photovoltaic solar system installed with the electric systemusing a geographic location of the electric system, an orientation ofthe prospective photovoltaic solar system, and a photovoltaic efficiencyof the prospective photovoltaic solar system. The photovoltaicproduction simulator 508 can also use historical weather data and anyother appropriate data to computationally simulate the production of thephotovoltaic solar system. In some examples, the installer 306 suppliesdata characterizing the photovoltaic efficiency of the prospectivephotovoltaic solar system.

The GUI 514 can be implemented using any appropriate user interfacetechnology, e.g., as one or more web pages hosted by a web server. Forexample, the GUI 514 can provide web pages for presentation on theinstaller's user device 308 of FIG. 3 or the manager's user device 310or both. The installer 306 or the manager 304 can then use the GUI 514to direct the data importer 506 to receive data for the manager'selectric system and prospective photovoltaic solar system.

For example, the manager 304 can use the GUI 514 to upload historicalconsumption data for electric energy consumption of the manager'selectric system. The installer 306 can use the GUI 514 to direct thephotovoltaic production simulator 508 to simulate one or more physicalconfigurations of the prospective photovoltaic solar system at a site ofthe electric system. The computer system 500 can then present results tothe installer 304 or the manager 308 or both using the GUI 514.

The machine-learning predictor 414 predicts electric energy import andexport values for the electric system with the prospective photovoltaicsolar system installed, or equivalently electric energy self-consumptionvalues. The electric energy values can be spaced apart by time intervalsat a low resolution. The machine-learning predictor 414 uses the lowresolution consumption data and the model built during the priortraining phase to predict the electric energy values.

The photovoltaic system analyzer 512 uses the predicted import timeseries to determine results for the electric system and the prospectivephotovoltaic solar system. For example, the photovoltaic system analyzer512 can predict a cost reduction for the prospective photovoltaic solarsystem using a utility rate structure, e.g., a utility rate structurethat imposes charges based on each unit of energy imported in a meteredinterval in addition to net-metering balancing. Predicting a costreduction can include determining a predicted cost of consumptionwithout the prospective photovoltaic solar system installed using thereceived consumption time series and the utility rate structure,determining a predicted cost of consumption with the prospectivephotovoltaic solar system installed using the predicted import andexport time series and the utility rate structure, and determining adifference between the two predicted costs.

In some examples, the photovoltaic system analyzer 512 determines atarget physical configuration of the prospective photovoltaic solarsystem. The photovoltaic system analyzer 512 can determine predictedcost reductions with several different sizes and orientations ofphotovoltaic solar systems, e.g., different numbers and/or sizes ofpanels and solar cells. For example, the installer 306 may supply datacharacterizing available sizes of panels and solar cells, and themanager 304 may supply data characterizing available area andorientations for the panels. Then, the photovoltaic system analyzer 512determines, as the target physical configuration, the configurationresulting in the greatest cost reduction.

The photovoltaic system analyzer 512 can also determine the targetphysical configuration using the best return on investment (ROI), or netpresent value (NPV). In some examples, if the manager 304 is subject toa tiered rate structure, the photovoltaic system analyzer 512 candetermine a minimum size to prevent the net consumption of the electricsystem from exceeding a threshold that triggers a higher billing rate.The target physical configuration can be specified using any appropriatemetric, e.g., by physical dimensions, Kilo-Watt rating, Kilo-Watt-Hourproduction, or any appropriate measure of energy production.

FIG. 6 is a flow diagram of an example method 600 for determiningelectric energy flow predictions for electric systems includingphotovoltaic solar systems. The method includes a training phase 602performed prior to a production phase 604. The same computer system mayperform both phases of the method 600; however, in some examples,different computer systems may perform the training and productionphases 602 and 604. For example, the computer system 400 of FIG. 4 canperform the training phase 602, and the computer system 500 of FIG. 5can perform the production phase 604.

The computer system receives electric energy consumption training dataand photovoltaic production training data (606). For example, thecomputer system can receive electric energy consumption training dataand photovoltaic production training data at a high resolution asdescribed above with reference to FIG. 4. The computer systemdownsamples the electric energy consumption training data andphotovoltaic production training data to a low resolution (608). Thecomputer system trains a machine-learning predictor to build a model tominimize self-consumption error between the electric energy consumptiontraining data, the photovoltaic production training data, and thedownsampled electric energy consumption training data and thedownsampled photovoltaic production training data (610).

The computer system receives a consumption time series of electricconsumption values for an electric system at the low resolution and apredicted production time series of electric production values for aprospective photovoltaic solar system for installation with the electricsystem (612). For example, the computer system can receive theconsumption time series and the predicted production time series asdescribed above with reference to FIGS. 3 and 5. The computer systemsupplies the consumption time series and the predicted production timeseries to the machine-learning predictor trained during the trainingphase (614). The computer system determines, using an output of themachine-learning predictor responsive to the consumption time series andthe predicted production time series, a predicted import time series ofelectric import values (616). The electric import values may be spacedapart by time intervals at the low resolution.

Although specific examples and features have been described above, theseexamples and features are not intended to limit the scope of the presentdisclosure, even where only a single example is described with respectto a particular feature. Examples of features provided in the disclosureare intended to be illustrative rather than restrictive unless statedotherwise. The above description is intended to cover such alternatives,modifications, and equivalents as would be apparent to a person skilledin the art having the benefit of this disclosure.

The scope of the present disclosure includes any feature or combinationof features disclosed in this specification (either explicitly orimplicitly), or any generalization of features disclosed, whether or notsuch features or generalizations mitigate any or all of the problemsdescribed in this specification. Accordingly, new claims may beformulated during prosecution of this application (or an applicationclaiming priority to this application) to any such combination offeatures. In particular, with reference to the appended claims, featuresfrom dependent claims may be combined with those of the independentclaims and features from respective independent claims may be combinedin any appropriate manner and not merely in the specific combinationsenumerated in the appended claims.

1-20. (canceled)
 21. A method comprising: receiving electric energy flowdata for a photovoltaic (PV) electric system at a first data resolutionfrom one or more sources; supplying the electric energy flow data to amachine-learning predictor trained during a prior training phase usingenergy flow training data at a second data resolution greater than thefirst data resolution; receiving an output responsive to the suppliedelectric flow energy data from the machine-learning predictor;determining, using the output of the machine-learning predictor, anenergy flow prediction at the first data resolution; exporting theenergy flow prediction for display on a user device.
 22. The method ofclaim 21, wherein receiving electric energy flow data from one or moresources includes receiving electric energy flow data from a user device,monitoring system associated with the PV electric system, an electricmeter coupled to the PV electric system, a utility computer system 316,or a combination thereof.
 23. The method of claim 21, wherein supplyingthe electric energy flow data to the machine-learning predictorcomprises supplying an energy consumption time series and a predictedenergy production time series for the PV electric system to themachine-learning predictor.
 24. The method of claim 23, wherein theenergy consumption time series has a lower resolution than a meteringresolution used by an electric utility at a site associated with the PVelectric system.
 25. The method of claim 21, wherein the determinedenergy flow prediction at the first data resolution is a predictedimport time series of electric import values each specifying an amountof electric energy predicted to be imported by the PV electric system.26. The method of claim 21, wherein the machine-learning predictor istrained during the prior training phase with electric energy flowtraining data downsampled to the first resolution, and wherein themachine-learning predictor is configured to build a model to minimizeself-consumption error between electric energy flow training data anddownsampled electric energy flow training data.
 27. The method of claim26, wherein the electric energy flow training data is downsampled to afirst resolution used by a utility metering system.
 28. The method ofclaim 21, wherein exporting the energy flow prediction for display on auser device comprises presenting to a user, in a graphical userinterface, a target physical configuration of the PV electric system.29. The method of claim 21, wherein exporting the energy flow predictionfor display on a user device comprises presenting a predicted energycost reduction by the PV electric system.
 30. The method of claim 29,wherein the predicted energy cost reduction is predicted based on theenergy flow prediction for the PV electrical system and a utility ratestructure.
 31. An electronic device, comprising processing circuitryconfigured to: transmit electric energy flow data for a photovoltaic(PV) electric system at a first data resolution to a server; the serverbeing configured to: supply the electric energy flow data to amachine-learning predictor trained during a prior training phase usingenergy flow training data at a second data resolution greater than thefirst data resolution; and, generate an energy flow prediction at thefirst data resolution, and display the energy flow prediction generatedby the server.
 32. The device of claim 31, wherein the electric energyflow data supplied to the machine-learning predictor comprises an energyconsumption time series and a predicted energy production time seriesfor the PV electric system.
 33. The method of claim 32, wherein theenergy consumption time series has a lower resolution than a meteringresolution used by an electric utility at a site associated with the PVelectric system.
 34. The method of claim 31, wherein the energy flowprediction generated by the server is a predicted import time series ofelectric import values each specifying an amount of electric energypredicted to be imported by the PV electric system.
 35. The method ofclaim 31, wherein the machine-learning predictor is trained during theprior training phase with electric energy flow training data downsampledto the first resolution, and wherein the machine-learning predictor isconfigured to build a model to minimize self-consumption error betweenelectric energy flow training data and downsampled electric energy flowtraining data.
 36. The method of claim 35, wherein the electric energyflow training data is downsampled to a first resolution used by autility metering system.
 37. The method of claim 31, wherein theprocessing circuitry is further configured to display a target physicalconfiguration of the PV electric system.
 38. The method of claim 31,wherein the processing circuitry is further configured to display apredicted energy cost reduction by the PV electric system.
 39. Themethod of claim 38, wherein the predicted energy cost reduction ispredicted based on the energy flow prediction for the PV electricalsystem and a utility rate structure.
 40. A photovoltaic (PV) electricsystem arranged according in a predetermined physical configurationcomprising: one or more PV panels; one or more inverters coupled to thePV panels; one or more loads for consuming alternating current (AC)power provided by the one or more inverters; an electric meter for theconnection to a utility grid, and wherein the predetermined physicalconfiguration of the PV electric system is determined from an energyflow prediction at a first data resolution output from amachine-learning predictor; the machine-learning predictor being trainedduring a training phase using energy flow training data at a second dataresolution greater than the first data resolution.